8.21 Principal Component Analysis (PCA) Using SVD & EVD | Linear Algebra for ML

Описание: In this lecture, we implement Principal Component Analysis (PCA) using both Eigenvalue Decomposition (EVD) and Singular Value Decomposition (SVD). You will clearly understand how PCA connects to eigenvalues, eigenvectors, singular values, and dimensionality reduction—an essential concept in Machine Learning, Data Science, and ML interviews.

Topics Covered:
1. Introduction to Eigenvalue Decomposition
2. Introduction to Singular Value Decomposition
3. Principal Component Analysis (PCA) Implementation Using EVD
4. Principal Component Analysis (PCA) Implementation Using SVD

Helpful For:
1. Cracking AI / ML / Data Science interview rounds at top tech companies
2. Building a deeper understanding of core AI, ML concepts
3. Preparing for GATE (DA / CS / Other streams) and other related competitive exams


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Tags:
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